This page shows the plots of covid case rate per 100k by state since the epidemic.
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library(plotly)##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
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## layoutknitr::opts_chunk$set(
fig.width = 8,
fig.asp = .8,
out.width = "90%"
)First, let’s input the CDC covid cases data by state with total time period till Thu Nov 18 2021.
[The data generated: Thu Nov 18 2021 22:02:06 GMT-0500 (EST)]
# input data table for Total Cases by State/Territory
covid_total_by_state =
read_csv("./data/united_states_covid19_cases_deaths_and_testing_by_state.csv", skip = 2) %>%
janitor::clean_names()## Rows: 62 Columns: 25## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (25): State/Territory, Level of Community Transmission, Total Cases, Con...##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.For the missing data, the original dataset fill it with “N/A”, so we replace it with NA and change the variable to numeric.
# figure out the missing data in data frame
na_strings = "N/A"
# fill in missing data
covid_total_by_state_tidy =
covid_total_by_state %>%
replace_with_na_all(condition = ~.x %in% na_strings) %>%
mutate(
state_territory = as.factor(state_territory),
case_rate_per_100000 = as.numeric(case_rate_per_100000)
) %>%
rename(region = "state_territory") %>%
select(region, case_rate_per_100000) %>%
mutate(region = recode(region, "New York*" = "New York"))Let’s make a choropleth map plot to show the total covid cases by state in US.
postal_code =
read_csv("./data/us_postal_code.csv") %>%
janitor::clean_names()## Rows: 51 Columns: 3## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): State, Abbrev, Code##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.#
plot_df =
left_join(postal_code, covid_total_by_state_tidy, by = c("state" = "region")) %>%
relocate(code)2.. Draw the plotly.
# specify some map projection/options
g = list(
scope = 'usa',
projection = list(type = 'albers usa'),
showlakes = TRUE,
lakecolor = toRGB('white')
)
plot_df$hover = with(plot_df, paste(
state, '<br>')
)
map_plotly1 =
plot_geo(plot_df, locationmode = 'USA-states') %>%
add_trace(
z = ~case_rate_per_100000, text = ~hover, locations = ~code,
color = ~case_rate_per_100000, colors = 'Purples'
) %>%
colorbar(title = "Case Rate Per 100K") %>%
layout(
title = "COVID-19 Case Rate by State/Territory (cases per 100,000)",
geo = g
)The following data frame is the covid case rate per 100k till Dec.31.2020.
# read data function
read_data_function = function(file_df) {
state_df =
read_csv(file_df, skip = 2)
return(state_df)
}
# create a data frame containing all participants
state_2020 =
tibble(
files = list.files("./data/state_covid_separate")
) %>%
mutate(
path = map(.x = files, ~paste("./data/state_covid_separate", ., sep = "/"))
) %>% # add path
mutate(
observations = map(path, read_data_function))Let’s unnest the data frame and filter the cases on Dec.31.2020
state_2020_tidy =
state_2020 %>%
unnest(cols = "observations") %>%
janitor::clean_names() %>%
filter(date == "Dec 31 2020") %>%
select(state, total_cases, total_case_rate_per_100k)Make map plot.
plot_2020_df = left_join(state_2020_tidy, postal_code, by = "state")# specify some map projection/options
g = list(
scope = 'usa',
projection = list(type = 'albers usa'),
showlakes = TRUE,
lakecolor = toRGB('white')
)
plot_2020_df$hover = with(plot_2020_df, paste(
state, '<br>', "Total case", total_cases, "<br>")
)
map_plotly2 =
plot_geo(plot_2020_df, locationmode = 'USA-states') %>%
add_trace(
z = ~total_case_rate_per_100k, text = ~hover, locations = ~code,
color = ~total_case_rate_per_100k, colors = 'Purples'
) %>%
colorbar(title = "Case Rate Per 100K") %>%
layout(
title = "2020 COVID-19 Case Rate by State/Territory (cases per 100,000)",
geo = g
)January 17, 2020 CDC begins screening passengers on direct and connecting flights from Wuhan, China at San Francisco, California, New York City, New York, and Los Angeles, California and plans to expand screening to other major airports. January 20, 2020 CDC confirms the first U.S. laboratory-confirmed case of COVID-19 in the U.S. from samples taken on January 18 in Washington state.
Dependent on a wide range of social diversion measures and state policy action indicators for COVID-19 testing and treatment, cases and deaths vary by state.
CDC COVID Data Tracker provides the covid cases data by state with total time period since the first covid case comfirmed till Thu Nov 18 2021.
This map shows the number of cases since the pandemic started for every 100,000 people, allowing you to compare areas with different population sizes. [The data generated: Thu Nov 18 2021 22:02:06 GMT-0500 (EST)]
The five regions with the highest incidence of new crowns are North Dakota, Alaska, Tennessee, Wyoming and South Dakota.
The following map shows the covid case rate per 100k in year 2020. [The data generated: Dec.31.2020]
The five regions with the highest incidence of new crowns are North Dakota, South Dakota, Wisconsin, Iowa and SNebraska.
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